改进SwinUNETR模型，提升脊柱3D分割精度与边界效果。
Adaptive Transformer Attention and Multi-Scale Fusion for Spine 3D Segmentation
- 引入多尺度融合与自适应注意力机制增强特征提取。
- 在mIoU、mDice和mAcc上优于3D CNN等基线模型。
- 适合医学图像分割研究者参考，尤其关注脊柱结构建模。
本研究提出一种基于改进SwinUNETR的脊柱3D语义分割方法，以提升分割精度与鲁棒性。针对脊柱影像复杂的解剖结构，引入多尺度融合机制，利用不同尺度信息增强特征表达能力，提高模型对目标区域的识别准确率；同时，设计自适应注意力机制，使模型可动态调整对关键区域的关注度，优化边界分割效果。实验结果表明，相较于3D CNN、3D U-Net及3D U-Net + Transformer，该模型在mIoU、mDice和mAcc指标上均取得显著提升，性能更优。消融实验证实多尺度融合与自适应注意力机制对分割任务有正向贡献。推理结果可视化分析显示，模型能更好还原脊柱真实解剖结构。未来可进一步优化Transformer结构并扩大数据规模，提升模型泛化能力。该研究为医学图像分割提供了高效解决方案，对智能医学影像分析具有重要意义。
原文摘要 · Abstract (English)
This study proposes a 3D semantic segmentation method for the spine based on the improved SwinUNETR to improve segmentation accuracy and robustness. Aiming at the complex anatomical structure of spinal images, this paper introduces a multi-scale fusion mechanism to enhance the feature extraction capability by using information of different scales, thereby improving the recognition accuracy of the model for the target area. In addition, the introduction of the adaptive attention mechanism enables the model to dynamically adjust the attention to the key area, thereby optimizing the boundary segmentation effect. The experimental results show that compared with 3D CNN, 3D U-Net, and 3D U-Net + Transformer, the model of this study has achieved significant improvements in mIoU, mDice, and mAcc indicators, and has better segmentation performance. The ablation experiment further verifies the effectiveness of the proposed improved method, proving that multi-scale fusion and adaptive attention mechanism have a positive effect on the segmentation task. Through the visualization analysis of the inference results, the model can better restore the real anatomical structure of the spinal image. Future research can further optimize the Transformer structure and expand the data scale to improve the generalization ability of the model. This study provides an efficient solution for the task of medical image segmentation, which is of great significance to intelligent medical image analysis.
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